Overview

Dataset statistics

Number of variables12
Number of observations7194164
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory713.5 MiB
Average record size in memory104.0 B

Variable types

Numeric11
Categorical1

Alerts

acceleration_y is highly correlated with acceleration_zHigh correlation
acceleration_z is highly correlated with acceleration_yHigh correlation
Accuracy is highly skewed (γ1 = 20.92819319) Skewed
Bearing has 588974 (8.2%) zeros Zeros
Speed has 1235824 (17.2%) zeros Zeros

Reproduction

Analysis started2021-12-10 12:46:33.803935
Analysis finished2021-12-10 12:58:29.680531
Duration11 minutes and 55.88 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

bookingID
Real number (ℝ≥0)

Distinct19962
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.182723734 × 1011
Minimum0
Maximum1.709396984 × 1012
Zeros443
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size109.8 MiB
2021-12-10T20:58:29.784310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.730941134 × 1010
Q13.779571222 × 1011
median8.074538517 × 1011
Q31.25413045 × 1012
95-th percentile1.606317769 × 1012
Maximum1.709396984 × 1012
Range1.709396984 × 1012
Interquartile range (IQR)8.761733282 × 1011

Descriptive statistics

Standard deviation4.951463486 × 1011
Coefficient of variation (CV)0.6051118976
Kurtosis-1.211616157
Mean8.182723734 × 1011
Median Absolute Deviation (MAD)4.294967295 × 1011
Skewness0.07856802874
Sum5.886785651 × 1018
Variance2.451699066 × 1023
MonotonicityNot monotonic
2021-12-10T20:58:29.924980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.380866644 × 10113267
 
< 0.1%
1.374389535 × 10122021
 
< 0.1%
3.435973847 × 10101813
 
< 0.1%
1.108101563 × 10121804
 
< 0.1%
7.473243096 × 10111696
 
< 0.1%
1.486058684 × 10121617
 
< 0.1%
8.933531977 × 10111550
 
< 0.1%
1.3657996 × 10121472
 
< 0.1%
1.211180778 × 10121407
 
< 0.1%
1.202590845 × 10111401
 
< 0.1%
Other values (19952)7176116
99.7%
ValueCountFrequency (%)
0443
< 0.1%
1389
< 0.1%
281
 
< 0.1%
4477
< 0.1%
6507
< 0.1%
7436
< 0.1%
8186
 
< 0.1%
10170
 
< 0.1%
1198
 
< 0.1%
14274
< 0.1%
ValueCountFrequency (%)
1.709396984 × 1012181
 
< 0.1%
1.709396984 × 1012474
 
< 0.1%
1.709396984 × 1012337
 
< 0.1%
1.709396984 × 1012332
 
< 0.1%
1.709396984 × 1012391
 
< 0.1%
1.709396984 × 1012109
 
< 0.1%
1.709396984 × 1012428
 
< 0.1%
1.709396984 × 1012250
 
< 0.1%
1.709396984 × 10121275
< 0.1%
1.709396984 × 1012451
 
< 0.1%

Accuracy
Real number (ℝ≥0)

SKEWED

Distinct38396
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.10795164
Minimum0.75
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 MiB
2021-12-10T20:58:30.070598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile3
Q13.9
median4.162
Q38
95-th percentile16
Maximum1500
Range1499.25
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation62.55569067
Coefficient of variation (CV)6.188760385
Kurtosis452.6193014
Mean10.10795164
Median Absolute Deviation (MAD)1.162
Skewness20.92819319
Sum72718261.78
Variance3913.214435
MonotonicityNot monotonic
2021-12-10T20:58:30.193479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.91294112
18.0%
3890962
12.4%
4845323
11.8%
10566057
 
7.9%
6549389
 
7.6%
8496499
 
6.9%
12194376
 
2.7%
16179944
 
2.5%
5131821
 
1.8%
976045
 
1.1%
Other values (38386)1969636
27.4%
ValueCountFrequency (%)
0.756
 
< 0.1%
0.827
 
< 0.1%
0.9139
 
< 0.1%
1336
< 0.1%
1.154
 
< 0.1%
1.2125
 
< 0.1%
1.3192
 
< 0.1%
1.4144
 
< 0.1%
1.5485
< 0.1%
1.6143
 
< 0.1%
ValueCountFrequency (%)
150063
< 0.1%
1497.8551
 
< 0.1%
1497.1771
 
< 0.1%
1489.3311
 
< 0.1%
148729
< 0.1%
1486.7691
 
< 0.1%
14852
 
< 0.1%
1482.4181
 
< 0.1%
147936
< 0.1%
1478.2431
 
< 0.1%

Bearing
Real number (ℝ≥0)

ZEROS

Distinct1059499
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.2125067
Minimum0
Maximum359.9994812
Zeros588974
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size109.8 MiB
2021-12-10T20:58:30.337743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q173
median164
Q3260.3179169
95-th percentile338
Maximum359.9994812
Range359.9994812
Interquartile range (IQR)187.3179169

Descriptive statistics

Standard deviation108.9644246
Coefficient of variation (CV)0.6595410162
Kurtosis-1.230906131
Mean165.2125067
Median Absolute Deviation (MAD)94
Skewness0.04111357366
Sum1188565868
Variance11873.24582
MonotonicityNot monotonic
2021-12-10T20:58:30.463895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0588974
 
8.2%
26825332
 
0.4%
8824413
 
0.3%
11924299
 
0.3%
11822586
 
0.3%
12222521
 
0.3%
25422339
 
0.3%
26721093
 
0.3%
12120918
 
0.3%
29920355
 
0.3%
Other values (1059489)6401334
89.0%
ValueCountFrequency (%)
0588974
8.2%
0.0012054741381
 
< 0.1%
0.0015040636067
 
< 0.1%
0.0016166716812
 
< 0.1%
0.0020672678951
 
< 0.1%
0.0023999959231
 
< 0.1%
0.0026849061251
 
< 0.1%
0.0030950754886
 
< 0.1%
0.0031604915861
 
< 0.1%
0.0033837407832
 
< 0.1%
ValueCountFrequency (%)
359.99948121
 
< 0.1%
359.99850461
 
< 0.1%
359.99847414
< 0.1%
359.99792482
< 0.1%
359.99737551
 
< 0.1%
359.99694821
 
< 0.1%
359.99551391
 
< 0.1%
359.99533081
 
< 0.1%
359.9951
 
< 0.1%
359.99490362
< 0.1%

acceleration_x
Real number (ℝ)

Distinct990842
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06777408473
Minimum-9.999999
Maximum13.3964945
Zeros6927
Zeros (%)0.1%
Negative3368295
Negative (%)46.8%
Memory size109.8 MiB
2021-12-10T20:58:30.858427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-9.999999
5-th percentile-1.7645805
Q1-0.50517654
median0.06190795898
Q30.63206923
95-th percentile1.892
Maximum13.3964945
Range23.3964935
Interquartile range (IQR)1.13724577

Descriptive statistics

Standard deviation1.350014832
Coefficient of variation (CV)19.91933697
Kurtosis13.02719684
Mean0.06777408473
Median Absolute Deviation (MAD)0.568322591
Skewness0.2185292787
Sum487577.8805
Variance1.822540046
MonotonicityNot monotonic
2021-12-10T20:58:30.986634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06927
 
0.1%
0.0454912223736
 
0.1%
0.0837996163725
 
0.1%
0.081405342737
 
< 0.1%
0.086193892647
 
< 0.1%
0.0430969452606
 
< 0.1%
0.0478854962597
 
< 0.1%
0.0981652662572
 
< 0.1%
0.088588172569
 
< 0.1%
0.050279772534
 
< 0.1%
Other values (990832)7161514
99.5%
ValueCountFrequency (%)
-9.9999991
 
< 0.1%
-9.999711
 
< 0.1%
-9.9993833
< 0.1%
-9.9990325931
 
< 0.1%
-9.9986421
 
< 0.1%
-9.9984911
 
< 0.1%
-9.9982849121
 
< 0.1%
-9.9981611
 
< 0.1%
-9.9981
 
< 0.1%
-9.9977081
 
< 0.1%
ValueCountFrequency (%)
13.39649451
< 0.1%
12.600731181
< 0.1%
12.432069771
< 0.1%
12.403222021
< 0.1%
12.193569581
< 0.1%
12.187738021
< 0.1%
12.165365581
< 0.1%
11.607110251
< 0.1%
11.597164031
< 0.1%
11.339548751
< 0.1%

acceleration_y
Real number (ℝ)

HIGH CORRELATION

Distinct922232
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.457620292
Minimum-19.99507446
Maximum24.939201
Zeros166
Zeros (%)< 0.1%
Negative1868136
Negative (%)26.0%
Memory size109.8 MiB
2021-12-10T20:58:31.130251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-19.99507446
5-th percentile-9.894955444
Q1-1.925576782
median9.088211
Q39.709717
95-th percentile10.433931
Maximum24.939201
Range44.93427546
Interquartile range (IQR)11.63529378

Descriptive statistics

Standard deviation8.068016364
Coefficient of variation (CV)1.809938002
Kurtosis-0.7401543253
Mean4.457620292
Median Absolute Deviation (MAD)0.850424
Skewness-1.064893748
Sum32068851.43
Variance65.09288805
MonotonicityNot monotonic
2021-12-10T20:58:31.251934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.7758243816
 
0.1%
9.739913808
 
0.1%
9.7423043802
 
0.1%
9.7279393781
 
0.1%
9.7375153779
 
0.1%
9.7686413731
 
0.1%
9.706393713
 
0.1%
9.7351223711
 
0.1%
9.7327273700
 
0.1%
9.7470933698
 
0.1%
Other values (922222)7156625
99.5%
ValueCountFrequency (%)
-19.995074461
< 0.1%
-19.8868471
< 0.1%
-19.822509771
< 0.1%
-19.777200321
< 0.1%
-19.738021851
< 0.1%
-19.732339481
< 0.1%
-19.682992551
< 0.1%
-19.614804081
< 0.1%
-19.61332
< 0.1%
-19.570092771
< 0.1%
ValueCountFrequency (%)
24.9392011
< 0.1%
24.9196131
< 0.1%
24.8731
< 0.1%
24.7246821
< 0.1%
24.6634251
< 0.1%
24.5741
< 0.1%
24.5245971
< 0.1%
24.4216021
< 0.1%
24.4168151
< 0.1%
24.2562221
< 0.1%

acceleration_z
Real number (ℝ)

HIGH CORRELATION

Distinct1328887
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8896587715
Minimum-14.99623108
Maximum14.999435
Zeros2081
Zeros (%)< 0.1%
Negative2729004
Negative (%)37.9%
Memory size109.8 MiB
2021-12-10T20:58:31.387571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-14.99623108
5-th percentile-4.188357544
Q1-0.923310125
median0.7781393
Q32.7458439
95-th percentile6.070498065
Maximum14.999435
Range29.99566608
Interquartile range (IQR)3.669154025

Descriptive statistics

Standard deviation3.173510117
Coefficient of variation (CV)3.567109344
Kurtosis1.116102528
Mean0.8896587715
Median Absolute Deviation (MAD)1.823695941
Skewness0.008618377417
Sum6400351.106
Variance10.07116646
MonotonicityNot monotonic
2021-12-10T20:58:31.520167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02081
 
< 0.1%
-0.088588171015
 
< 0.1%
-0.050279771005
 
< 0.1%
1.1779832854
 
< 0.1%
1.2905141834
 
< 0.1%
-0.9122187824
 
< 0.1%
1.0654523824
 
< 0.1%
1.211503819
 
< 0.1%
0.41420954817
 
< 0.1%
0.4932206816
 
< 0.1%
Other values (1328877)7184275
99.9%
ValueCountFrequency (%)
-14.996231081
< 0.1%
-14.988604741
< 0.1%
-14.978884891
< 0.1%
-14.977987671
< 0.1%
-14.9761892
< 0.1%
-14.964230351
< 0.1%
-14.957202151
< 0.1%
-14.957052611
< 0.1%
-14.956005861
< 0.1%
-14.955557251
< 0.1%
ValueCountFrequency (%)
14.9994351
< 0.1%
14.9937591
< 0.1%
14.9933011
< 0.1%
14.9905551
< 0.1%
14.9857662
< 0.1%
14.9856571
< 0.1%
14.9833721
< 0.1%
14.9823161
< 0.1%
14.9781262
< 0.1%
14.9779151
< 0.1%

gyro_x
Real number (ℝ)

Distinct2039457
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.001738068972
Minimum-0.9998474
Maximum1.311228297
Zeros19937
Zeros (%)0.3%
Negative3697871
Negative (%)51.4%
Memory size109.8 MiB
2021-12-10T20:58:31.664822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.9998474
5-th percentile-0.136856839
Q1-0.02654852027
median-0.00065484754
Q30.023071289
95-th percentile0.13194689
Maximum1.311228297
Range2.311075697
Interquartile range (IQR)0.04961980927

Descriptive statistics

Standard deviation0.1000226227
Coefficient of variation (CV)-57.54813204
Kurtosis17.42008449
Mean-0.001738068972
Median Absolute Deviation (MAD)0.02479807354
Skewness-0.006271419179
Sum-12503.95323
Variance0.01000452505
MonotonicityNot monotonic
2021-12-10T20:58:31.792487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019937
 
0.3%
-0.00122173058225
 
0.1%
0.00122173057626
 
0.1%
-0.0024434617141
 
0.1%
0.0024434616615
 
0.1%
-0.0048869225520
 
0.1%
0.0048869225342
 
0.1%
-0.00610865234686
 
0.1%
0.00610865234681
 
0.1%
0.0085521144495
 
0.1%
Other values (2039447)7119896
99.0%
ValueCountFrequency (%)
-0.99984741
< 0.1%
-0.99983429651
< 0.1%
-0.99982204591
< 0.1%
-0.999742031
< 0.1%
-0.99974061
< 0.1%
-0.999710261
< 0.1%
-0.99969481
< 0.1%
-0.999672231
< 0.1%
-0.99965431
< 0.1%
-0.999650661
< 0.1%
ValueCountFrequency (%)
1.3112282971
< 0.1%
1.1650978491
< 0.1%
1.1644024891
< 0.1%
1.1556707631
< 0.1%
0.99988729341
< 0.1%
0.99983981
< 0.1%
0.99981191
< 0.1%
0.99979887641
< 0.1%
0.99975679851
< 0.1%
0.99969931
< 0.1%

gyro_y
Real number (ℝ)

Distinct2203565
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.000127149945
Minimum-5.9894347
Maximum5.99646
Zeros18555
Zeros (%)0.3%
Negative3537346
Negative (%)49.2%
Memory size109.8 MiB
2021-12-10T20:58:31.926605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-5.9894347
5-th percentile-0.1753217136
Q1-0.02970117037
median0.0002593994
Q30.031083878
95-th percentile0.1724843434
Maximum5.99646
Range11.9858947
Interquartile range (IQR)0.06078504837

Descriptive statistics

Standard deviation0.1781302481
Coefficient of variation (CV)-1400.946325
Kurtosis222.4634773
Mean-0.000127149945
Median Absolute Deviation (MAD)0.0304012354
Skewness-0.05436814525
Sum-914.7375568
Variance0.03173038529
MonotonicityNot monotonic
2021-12-10T20:58:32.057257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018555
 
0.3%
-0.00122173057083
 
0.1%
0.00122173057074
 
0.1%
-0.0024434616287
 
0.1%
0.0024434616103
 
0.1%
-0.0048869224974
 
0.1%
0.0048869224731
 
0.1%
-0.00610865234239
 
0.1%
0.00610865234011
 
0.1%
-0.0085521143968
 
0.1%
Other values (2203555)7127139
99.1%
ValueCountFrequency (%)
-5.98943471
< 0.1%
-5.98453041
< 0.1%
-5.98446041
< 0.1%
-5.98230651
< 0.1%
-5.9760641
< 0.1%
-5.9738081
< 0.1%
-5.97288041
< 0.1%
-5.96110771
< 0.1%
-5.9524071
< 0.1%
-5.9498781
< 0.1%
ValueCountFrequency (%)
5.996461
< 0.1%
5.99098871
< 0.1%
5.99093631
< 0.1%
5.98913241
< 0.1%
5.98603531
< 0.1%
5.98376461
< 0.1%
5.9772671
< 0.1%
5.97701741
< 0.1%
5.97187141
< 0.1%
5.9701571
< 0.1%

gyro_z
Real number (ℝ)

Distinct1746404
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0003658102102
Minimum-6.9389644
Maximum6.690163193
Zeros22688
Zeros (%)0.3%
Negative3602888
Negative (%)50.1%
Memory size109.8 MiB
2021-12-10T20:58:32.197925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.9389644
5-th percentile-0.09352112
Q1-0.018467678
median-2.4430454 × 10-5
Q30.0181091
95-th percentile0.09188748676
Maximum6.690163193
Range13.62912759
Interquartile range (IQR)0.036576778

Descriptive statistics

Standard deviation0.102658583
Coefficient of variation (CV)-280.6334544
Kurtosis396.894181
Mean-0.0003658102102
Median Absolute Deviation (MAD)0.01830137555
Skewness-0.6651113066
Sum-2631.698645
Variance0.01053878466
MonotonicityNot monotonic
2021-12-10T20:58:32.331659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022688
 
0.3%
0.00122173058481
 
0.1%
-0.00122173057925
 
0.1%
0.0024434617299
 
0.1%
-0.0024434617189
 
0.1%
-0.0048869225983
 
0.1%
0.0048869225914
 
0.1%
-0.00610865235476
 
0.1%
-0.0085521145006
 
0.1%
0.00610865234948
 
0.1%
Other values (1746394)7113255
98.9%
ValueCountFrequency (%)
-6.93896441
< 0.1%
-6.9192041
< 0.1%
-6.86593061
< 0.1%
-6.8095731
< 0.1%
-6.73604871
< 0.1%
-6.7026166671
< 0.1%
-6.6966011
< 0.1%
-6.51878551
< 0.1%
-6.38720371
< 0.1%
-6.3549581
< 0.1%
ValueCountFrequency (%)
6.6901631931
< 0.1%
6.60634471
< 0.1%
6.60583731
< 0.1%
6.60150241
< 0.1%
6.49171731
< 0.1%
6.4286131
< 0.1%
6.4244871
< 0.1%
6.3905091
< 0.1%
6.2878421
< 0.1%
6.1432590951
< 0.1%

second
Real number (ℝ≥0)

Distinct6090
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean600.8619229
Minimum0
Maximum8234
Zeros8840
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size109.8 MiB
2021-12-10T20:58:32.466782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43
Q1241
median519
Q3861
95-th percentile1437
Maximum8234
Range8234
Interquartile range (IQR)620

Descriptive statistics

Standard deviation461.5591954
Coefficient of variation (CV)0.7681618318
Kurtosis8.746515994
Mean600.8619229
Median Absolute Deviation (MAD)303
Skewness1.573518271
Sum4322699215
Variance213036.8909
MonotonicityNot monotonic
2021-12-10T20:58:32.588459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08840
 
0.1%
128526
 
0.1%
88504
 
0.1%
68503
 
0.1%
108476
 
0.1%
58449
 
0.1%
28448
 
0.1%
148445
 
0.1%
208428
 
0.1%
98422
 
0.1%
Other values (6080)7109123
98.8%
ValueCountFrequency (%)
08840
0.1%
18325
0.1%
28448
0.1%
38336
0.1%
48399
0.1%
58449
0.1%
68503
0.1%
78267
0.1%
88504
0.1%
98422
0.1%
ValueCountFrequency (%)
82341
< 0.1%
82331
< 0.1%
82321
< 0.1%
82301
< 0.1%
82281
< 0.1%
82231
< 0.1%
82191
< 0.1%
82161
< 0.1%
82151
< 0.1%
82131
< 0.1%

Speed
Real number (ℝ)

ZEROS

Distinct3103928
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.022868019
Minimum-15.47626758
Maximum92.44881638
Zeros1235824
Zeros (%)17.2%
Negative100931
Negative (%)1.4%
Memory size109.8 MiB
2021-12-10T20:58:32.724864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-15.47626758
5-th percentile0
Q11.042856475
median7.559999943
Q315.4936015
95-th percentile23.5031688
Maximum92.44881638
Range107.925084
Interquartile range (IQR)14.45074502

Descriptive statistics

Standard deviation8.104774683
Coefficient of variation (CV)0.8982481697
Kurtosis-0.9499395382
Mean9.022868019
Median Absolute Deviation (MAD)7.067878723
Skewness0.5014910725
Sum64911992.28
Variance65.68737267
MonotonicityNot monotonic
2021-12-10T20:58:32.857509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01235824
 
17.2%
-199101
 
1.4%
0.54974
 
0.1%
13809
 
0.1%
1.52920
 
< 0.1%
0.252732
 
< 0.1%
22672
 
< 0.1%
0.752388
 
< 0.1%
2.52363
 
< 0.1%
1.252305
 
< 0.1%
Other values (3103918)5835076
81.1%
ValueCountFrequency (%)
-15.476267581
< 0.1%
-15.107533151
< 0.1%
-10.622251
< 0.1%
-10.377667691
< 0.1%
-10.065247831
< 0.1%
-9.9947829481
< 0.1%
-9.8721262061
< 0.1%
-9.6621875071
< 0.1%
-9.66124751
< 0.1%
-9.0880189861
< 0.1%
ValueCountFrequency (%)
92.448816381
< 0.1%
82.868359871
< 0.1%
82.752980191
< 0.1%
74.452879891
< 0.1%
73.057143991
< 0.1%
66.037399911
< 0.1%
63.361307791
< 0.1%
58.506704191
< 0.1%
57.621919931
< 0.1%
57.620654041
< 0.1%

label
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.8 MiB
0
5006289 
1
2187875 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
05006289
69.6%
12187875
30.4%

Length

2021-12-10T20:58:32.988929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-10T20:58:33.059698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05006289
69.6%
12187875
30.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-12-10T20:57:35.988809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:52:15.370571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:52:48.879179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:53:20.119521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:53:53.396791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:54:26.188726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:54:58.925086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:55:31.581015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:56:03.045481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:56:33.241412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:57:03.669659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-10T20:52:54.412519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-10T20:54:05.286536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-10T20:56:44.044624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-10T20:53:35.105107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:54:08.275434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:54:41.001871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-10T20:55:22.684886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:55:54.467855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:56:24.981698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:56:54.878385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:57:27.464465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:58:02.077338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:52:42.949667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:53:14.010704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:53:47.270516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:54:20.243955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:54:52.934815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:55:25.711540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:55:57.331731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:56:27.793972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:56:57.753151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:57:30.270021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:58:04.843752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:52:46.046567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:53:17.089603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:53:50.368145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:54:23.156973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:54:55.925193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:55:28.614799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:56:00.185462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:56:30.569299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:57:00.569215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-10T20:57:33.119713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-12-10T20:58:33.139485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-10T20:58:33.515602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-10T20:58:33.740508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-10T20:58:33.984855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-10T20:58:05.194566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-10T20:58:09.516539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

bookingIDAccuracyBearingacceleration_xacceleration_yacceleration_zgyro_xgyro_ygyro_zsecondSpeedlabel
02.748779e+119.29317.0000000.0327758.6599334.7373000.0246290.004028-0.010858257.00.1900001
11.632088e+1110.000262.1844180.599490-9.180023-2.5204320.026767-0.030687-0.006071556.02.1100000
21.632088e+113.900247.000000-0.1789709.1497043.551163-0.006271-0.026047-0.01231432.09.0500001
32.920578e+114.000248.0000000.2705533.2705798.959376-0.0094040.023554-0.0069461008.09.4974101
42.491081e+116.00095.000000-0.1527989.903376-0.042253-0.0068940.0635130.028047518.00.0486651
59.448928e+107.627114.000000-0.50902610.6935201.033156-0.1743580.0348370.0246621250.023.4394100
68.589935e+094.000352.000000-1.40382610.184417-0.6078110.3321540.162473-0.033859782.021.0563801
74.638565e+1112.00073.818901-0.793289-8.862109-3.1200710.0144060.0155870.003032876.023.2225000
81.288490e+115.0020.000000-0.2334357.5142026.165070-0.019743-0.040549-0.049944108.00.0000000
95.153961e+109.648105.000000-1.26486211.3376160.5589290.0069270.0207980.010254719.021.9800000

Last rows

bookingIDAccuracyBearingacceleration_xacceleration_yacceleration_zgyro_xgyro_ygyro_zsecondSpeedlabel
71941541.133871e+126.00042.0000000.36836212.377152-0.3915380.0268960.090635-0.0968481404.025.3857251
71941551.365800e+128.0000.000000-0.202322-8.624347-4.963852-0.020651-0.0369870.036801611.028.1591850
71941561.486059e+126.00088.000000-0.1963319.1724673.7087320.0517270.012014-0.0263041252.028.9851231
71941571.589138e+126.000355.0000000.5411068.7558631.0846070.048869-0.066584-0.0409281062.026.8682521
71941581.348620e+126.00041.0000000.1209078.8453784.4987050.024435-0.0122170.006109323.025.6125321
71941591.211181e+1211.000119.0000000.7164657.2819642.324770-0.596548-0.0300940.091613541.025.2500001
71941601.494649e+124.0000.0000000.5027985.8109057.5156290.0072950.0355050.0264561637.027.7946931
71941611.228361e+124.964272.0000000.06703811.4059770.1915360.075695-0.0237190.054524742.025.2817690
71941621.511828e+127.00049.7033690.007925-8.0332310.388345-0.0503940.0247470.028131499.025.5961360
71941631.460289e+126.000246.0000000.0143668.2817960.979258-0.074037-0.002198-0.041655943.025.0089701